Processing Results from DADA2 to make plots, do some statistics

Link to notebook

Link to github repo.

Table of Contents

• Load packages
• Import and prepare the data from eDNA
  • Import metadata
  • Import DADA2 results
  • Make phyloseq object
• QC and filtering eDNA dataset
  • Rarefaction curves
  • Filtering
  • Check sequencing effort
• Abundance plots eDNA
  • Abundance at family level
  • Bubble plots
    • Bubble plot without Elasmobranchs
• Import and prepare the data from trawls
  • Import Trawl Count Data
• Abundance plots Trawls
  • Bubble plots of Counts
  • Bubble plots of CPUE
• Compare Trawl and eDNA
• Prepare the data
• Species Richness
• Species tally across whole study
• Exploratory Analyses
  • Ordinations on eDNA
    • PCA
    • PCoA Jaccard
    • PCoA Bray Curtis
    • NMDS Aitchison
    • NMDS Jacaard
    • NMDS Bray Curtis
    • eDNA Ordinations Summary
  • PCoA with Environmental Variables
  • Ordinations on CPUE

Load packages

library(tidyverse)
library(readxl)
library(phyloseq)
library(Biostrings)
#library(phangorn)
library(readr)
library(seqinr)
library(decontam)
library(ape)
library(vegan)
#library(philr)
library(RColorBrewer)
library(microbiome)
#library(DESeq2)
library(compositions);
library(cowplot)
library(plotly)
library(htmlwidgets)
library(withr)
library(lubridate)

Import and prepare the data from eDNA

Import metadata

metadata <- read_csv("sample_data.csv")

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  SampleID = col_character(),
  `Year.Trawl#` = col_character(),
  Datecode = col_double(),
  Date = col_character(),
  Month = col_double(),
  Year = col_double(),
  Bayside = col_character(),
  Station = col_character(),
  Habitat = col_character(),
  DO = col_double(),
  Salinity = col_double(),
  Temperature = col_double()
)

Import DADA2 results

Import count table and taxonomy file. I slightly modified otutable.csv in Excel to otutable_mod.csv to remove the quotes around seq names and put NA placehoder as first col name (which was above row names)

# Import Count table. Skip first row of tsv file, which is just some text
count_table <- read_table2("results/otutable_mod.csv")

Missing column names filled in: 'X1' [1]
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  X1 = col_character()
)
ℹ Use `spec()` for the full column specifications.

colnames(count_table)[1] <- "SampleID"

# Import taxonomy of ASVs
taxonomy <- read_csv(file="results/tax_sequences_blast_taxonomy.csv")

Missing column names filled in: 'X1' [1]Duplicated column names deduplicated: 'RefSeq_Tax_ID' => 'RefSeq_Tax_ID_1' [18]
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  X1 = col_double(),
  ASV_ID = col_character(),
  ref_seq_ID = col_character(),
  PID = col_double(),
  alnmt_len = col_double(),
  mismatch = col_double(),
  eval = col_double(),
  bscore = col_double(),
  RefSeq_Tax_ID = col_double(),
  Ref_Seq_title = col_character(),
  superkingdom = col_character(),
  phylum = col_character(),
  class = col_character(),
  order = col_character(),
  family = col_character(),
  genus = col_character(),
  species = col_character(),
  RefSeq_Tax_ID_1 = col_double()
)

# remove first col of sequential numbers
taxonomy[,1] <- NULL
# filter out sequences with low PID (recommended by Sara)
taxonomy <- filter(taxonomy, PID > 92)

# remove BLAST metadata and just retain taxonomy (necessary for further processing below)
drop.cols <- c(colnames(taxonomy)[2:9],'RefSeq_Tax_ID_1')
taxonomy <-  select(taxonomy, -one_of(drop.cols))


# And import the Common names, as curated by Sara. Join to taxonomy
commonnames <- read_excel("Trawls MASTER 2020 _mod_ES.xlsx",7)
commonnames

taxonomy <- left_join(taxonomy, commonnames, by = "ASV_ID")
taxonomy

NA

Filtering removed seqs 110, 332 (Gobiosoma ginsburgi and Belone belone) Note for Sara should we consider setting this at 97% which is more robust and still leaves 334 unique ASVs (rather than 379 with the 92% cutoff in the settings above)

Preview datasets

count_table

taxonomy

metadata

Make phyloseq object

I want to use the phyloseq package for some plotting/ statistics, which first requires making phyloseq objects out of each of input data tables-

count_table_matrix <- as.matrix(count_table[,2:392]) # convert count table to matrix, leaving out character column of sample ID
rownames(count_table_matrix) <- count_table$SampleID # add back in Sample IDs as row names
ASV =   otu_table(count_table_matrix, taxa_are_rows =  FALSE)

taxonomy_matrix <- as.matrix(taxonomy[,2:9])
rownames(taxonomy_matrix) <- taxonomy$ASV_ID 
TAX =   tax_table(taxonomy_matrix)

# select only the metada rows with eDNA samples
metadata_edna <- metadata %>% filter(!is.na(SampleID))

META    =   sample_data(data.frame(metadata_edna, row.names = metadata_edna$`SampleID`))

First check that the inputs are in compatible formats by checking for ASV names with the phyloseq function, taxa_names

head(taxa_names(TAX))

[1] "Seq_1" "Seq_2" "Seq_3" "Seq_4" "Seq_5" "Seq_6"

head(taxa_names(ASV))

[1] "Seq_1" "Seq_2" "Seq_3" "Seq_4" "Seq_5" "Seq_6"

And check sample names were also detected

# Modify taxa names in ASV, which are formatted with the sample ID, underscor, fastq ID. Don't need this fastq ID anymore and want it to match the sample names from metadata
sample_names(ASV) <-  sample_names(ASV) %>%
  str_replace_all(pattern = "_S[:digit:]+",replacement = "")


head(sample_names(ASV))

[1] "T1PosCon" "T1S10"    "T1S11"    "T1S1"     "T1S2"     "T1S3"    

head(sample_names(META))

[1] "T1PosCon" "T1S1"     "T1S2"     "T1S3"     "T1S5"     "T1S6"    

And make the phyloseq object

ps <- phyloseq(ASV, TAX,    META)

QC and filtering eDNA dataset

Rarefaction curves

rarecurve(otu_table(ps), step=50, cex=0.5)

empty rows removed

# save as .eps
setEPS()
postscript("Figures/rarefaction.eps")

rarecurve(otu_table(ps), step=50, cex=0.5)

empty rows removed

dev.off()

quartz_off_screen 
                2 

Most samples look like they were sampled to completion. Be weary of T3S11, T1S2, and maybe T4S5

Filtering

Check some features of the phyloseq object

rank_names(ps)

[1] "superkingdom" "phylum"       "class"        "order"        "family"       "genus"        "species"      "CommonName"  

unique(tax_table(ps)[, "superkingdom"])

Taxonomy Table:     [2 taxa by 1 taxonomic ranks]:
        superkingdom
Seq_1   "Eukaryota" 
Seq_377 NA          

unique(tax_table(ps)[, "phylum"])

Taxonomy Table:     [3 taxa by 1 taxonomic ranks]:
        phylum      
Seq_1   "Chordata"  
Seq_368 "Arthropoda"
Seq_377 NA          

unique(tax_table(ps)[, "class"])

Taxonomy Table:     [5 taxa by 1 taxonomic ranks]:
        class           
Seq_1   "Actinopteri"   
Seq_63  "Mammalia"      
Seq_362 "Chondrichthyes"
Seq_368 "Insecta"       
Seq_377 NA              

There are some ASVs with NA as superkingdom, phylum, or class annotation- delete these.

ps <- subset_taxa(ps, !is.na(superkingdom) & !is.na(phylum) & !is.na(class))

unique(tax_table(ps)[, "superkingdom"])

Taxonomy Table:     [1 taxa by 1 taxonomic ranks]:
      superkingdom
Seq_1 "Eukaryota" 

unique(tax_table(ps)[, "phylum"])

Taxonomy Table:     [2 taxa by 1 taxonomic ranks]:
        phylum      
Seq_1   "Chordata"  
Seq_368 "Arthropoda"

unique(tax_table(ps)[, "class"])

Taxonomy Table:     [4 taxa by 1 taxonomic ranks]:
        class           
Seq_1   "Actinopteri"   
Seq_63  "Mammalia"      
Seq_362 "Chondrichthyes"
Seq_368 "Insecta"       

nrow(tax_table(ps)) # number of ASVs left

[1] 378

378 ASVs still remain…

Also check class Mammalia, to see if they are contamination or real:

tax_table(subset_taxa(ps, class == 'Mammalia'))

Taxonomy Table:     [8 taxa by 8 taxonomic ranks]:
        superkingdom phylum     class      order          family      genus   species        CommonName 
Seq_63  "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    
Seq_88  "Eukaryota"  "Chordata" "Mammalia" "Artiodactyla" "Suidae"    "Sus"   "Sus scrofa"   "Wild boar"
Seq_157 "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    
Seq_343 "Eukaryota"  "Chordata" "Mammalia" "Carnivora"    "Felidae"   "Felis" "Felis catus"  "Cat"      
Seq_369 "Eukaryota"  "Chordata" "Mammalia" "Artiodactyla" "Bovidae"   "Bos"   "Bos taurus"   "Cattle"   
Seq_378 "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    
Seq_383 "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    
Seq_389 "Eukaryota"  "Chordata" "Mammalia" "Primates"     "Hominidae" "Homo"  "Homo sapiens" "Human"    

These are human, wild boar, cat (ahem…cat lady), and cattle. All are contamination so delete all Mammalia

ps <- subset_taxa(ps, !class == 'Mammalia')
unique(tax_table(ps)[, "class"])

Taxonomy Table:     [3 taxa by 1 taxonomic ranks]:
        class           
Seq_1   "Actinopteri"   
Seq_362 "Chondrichthyes"
Seq_368 "Insecta"       

Next check the “Insecta” entries

tax_table(subset_taxa(ps, class == 'Insecta'))

Taxonomy Table:     [2 taxa by 8 taxonomic ranks]:
        superkingdom phylum       class     order         family       genus         species              CommonName
Seq_368 "Eukaryota"  "Arthropoda" "Insecta" "Hymenoptera" "Formicidae" "Linepithema" "Linepithema humile" "Ant"     
Seq_380 "Eukaryota"  "Arthropoda" "Insecta" "Hymenoptera" "Formicidae" "Linepithema" "Linepithema humile" "Ant"     

The onlly Insecta is Linepithema humile, which are ants so delete these too..

ps <- subset_taxa(ps, !class == 'Insecta')
unique(tax_table(ps)[, "class"])

Taxonomy Table:     [2 taxa by 1 taxonomic ranks]:
        class           
Seq_1   "Actinopteri"   
Seq_362 "Chondrichthyes"

Check sequencing effort

Check overall how many ASVs there are per sample

# First aglomerate the ASVs at the phylum level using the phyloseq function, tax_glom
superkingdomGlommed = tax_glom(ps, "superkingdom")

# and plot
plot_bar(superkingdomGlommed, x = "Sample")

ggsave(filename = "Figures/seqdepth.eps", plot = plot_bar(superkingdomGlommed, x = "Sample"), units = c("in"), width = 9, height = 6, dpi = 300, )# and save

Total sequences reveals certain samples had very low sequencing effort: T1S7, T1S8, T3S11, and, not as bad, T1S2 and T4S5

The rarefaction analysis also showed T1S2 and T4S5 samples were likely not sequenced to completion. Therefore remove these 5 samples from analysis

ps <- subset_samples(ps, !SampleID == "T1S7" & !SampleID == "T1S8" & !SampleID == "T3S11" & !SampleID == "T1S2" & !SampleID == "T4S5")

ps

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 368 taxa and 50 samples ]
sample_data() Sample Data:       [ 50 samples by 12 sample variables ]
tax_table()   Taxonomy Table:    [ 368 taxa by 8 taxonomic ranks ]

50 samples remaining with 368 ASVs

Remove Pos Controls (all hits in positive controls are the same family- I assume this is expected)

ps <- subset_samples(ps, !SampleID == "T1PosCon" & !SampleID == "T2PosCon" & !SampleID == "T3PosCon")
ps

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 368 taxa and 47 samples ]
sample_data() Sample Data:       [ 47 samples by 12 sample variables ]
tax_table()   Taxonomy Table:    [ 368 taxa by 8 taxonomic ranks ]

47 samples remaining with 368 unique ASVs

And lastly, correct some taxonomy: **First* according to Sara, Engraulis encrasicolus (European anchovy) and Engraulis mordax should be Anchoa mitchilli (Bay anchovy):

tax_table(ps) <- gsub(tax_table(ps), pattern = "Engraulis encrasicolus", replacement = "Anchoa mitchilli")  
tax_table(ps) <- gsub(tax_table(ps), pattern = "Engraulis mordax", replacement = "Anchoa mitchilli")  

Second the Fourhorn sculpin (Myoxocephalus quadricornis) is actually an Arctic species. This ASV has 100% PID and 100% query cover to Myoxocephalus quadricornis & Myoxocephalus scorpius (another Arctic species) and 99.4% PID, 100% query cover to Myoxocephalus aenaeus. This latter one is actually the regional species, so this is more likely to be the identity:

tax_table(ps) <- gsub(tax_table(ps), pattern = "Myoxocephalus quadricornis", replacement = "Myoxocephalus aenaeus") 
tax_table(ps) <- gsub(tax_table(ps), pattern = "Fourhorn sculpin", replacement = "Grubby sculpin") 

Third Scomber japonicus, the chub mackerel, is only found in the Indo-Pacific. While this is a commercial product and could be here due to sewage, it is more likely the Scomber colias (Atlantic chub mackerel), which is found regionally (in the open ocean Atlantic). The blast hit to Scomber japonicus has PID of 100% and query cover of 100% while the similarity to Scomber colias 100% query cover/ 99.41% PID.

tax_table(ps) <- gsub(tax_table(ps), pattern = "Scomber japonicus", replacement = "Scomber colias") 
tax_table(ps) <- gsub(tax_table(ps), pattern = "Chub mackerel", replacement = "Atlantic chub mackerel") 

ps

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 368 taxa and 47 samples ]
sample_data() Sample Data:       [ 47 samples by 12 sample variables ]
tax_table()   Taxonomy Table:    [ 368 taxa by 8 taxonomic ranks ]

47 samples remainwith 368 unique ASVs

Abundance plots eDNA

For plotting, use relative abundances (# of ASV sequences/sum total sequences in sample), calculated easily using microbiome::transform

ps_ra <- microbiome::transform(ps, transform = "compositional")

Export the relative abundance matrix so Sara can have it:

# Extract abundance matrix from the phyloseq object
RelAbun_matrix = as(otu_table(ps_ra), "matrix")

# Coerce to data.frame
RelAbun_dataframe = as.data.frame(RelAbun_matrix)

# Export
write.csv(RelAbun_dataframe,"results/otutable_relabun.csv", row.names = TRUE)

Abundance at family level

Then aglomerate the ASVs at the family level using the phyloseq function, tax_glom

familyGlommed_RA = tax_glom(ps_ra, "family")
family_barplot <- plot_bar(familyGlommed_RA, x = "Sample", fill = "family")
family_barplot

NOTES

• There are some samples, (T1S3, T1S6, T2S11, T3S10, T3S4, T3S5, T3S9, T4S4, T4S7, T5S7) which are composed almost exclusively of 1 family. This might be fine, but I’m not used to seeing this with prokaroytic data. Just want to check with you

Agglomerate by species to take a look at the unique species

speciesGlommed_RA = tax_glom(ps_ra, "CommonName")
speciesGlommed_RA

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 41 taxa and 47 samples ]
sample_data() Sample Data:       [ 47 samples by 12 sample variables ]
tax_table()   Taxonomy Table:    [ 41 taxa by 8 taxonomic ranks ]

tax_table(speciesGlommed_RA)

Taxonomy Table:     [41 taxa by 8 taxonomic ranks]:
        superkingdom phylum     class            order                family            genus               
Seq_1   "Eukaryota"  "Chordata" "Actinopteri"    "Atheriniformes"     "Atherinopsidae"  "Menidia"           
Seq_2   "Eukaryota"  "Chordata" "Actinopteri"    "Clupeiformes"       "Clupeidae"       "Brevoortia"        
Seq_3   "Eukaryota"  "Chordata" "Actinopteri"    "Clupeiformes"       "Engraulidae"     "Engraulis"         
Seq_4   "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Pomatomidae"     "Pomatomus"         
Seq_5   "Eukaryota"  "Chordata" "Actinopteri"    "Lutjaniformes"      "Lutjanidae"      "Lutjanus"          
Seq_6   "Eukaryota"  "Chordata" "Actinopteri"    "Pleuronectiformes"  "Paralichthyidae" "Paralichthys"      
Seq_7   "Eukaryota"  "Chordata" "Actinopteri"    "Clupeiformes"       "Clupeidae"       "Alosa"             
Seq_9   "Eukaryota"  "Chordata" "Actinopteri"    "Gobiiformes"        "Gobiidae"        "Gobiosoma"         
Seq_10  "Eukaryota"  "Chordata" "Actinopteri"    "Pleuronectiformes"  "Scophthalmidae"  "Scophthalmus"      
Seq_11  "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Serranidae"      "Centropristis"     
Seq_12  "Eukaryota"  "Chordata" "Actinopteri"    "Spariformes"        "Sparidae"        "Stenotomus"        
Seq_15  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Sciaenidae"      "Leiostomus"        
Seq_16  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Sciaenidae"      "Menticirrhus"      
Seq_17  "Eukaryota"  "Chordata" "Actinopteri"    "Labriformes"        "Labridae"        "Tautoga"           
Seq_19  "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Cottidae"        "Myoxocephalus"     
Seq_20  "Eukaryota"  "Chordata" "Actinopteri"    "Pleuronectiformes"  "Pleuronectidae"  "Pseudopleuronectes"
Seq_21  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Moronidae"       "Morone"            
Seq_22  "Eukaryota"  "Chordata" "Actinopteri"    "Syngnathiformes"    "Syngnathidae"    "Syngnathus"        
Seq_30  "Eukaryota"  "Chordata" "Actinopteri"    "Pleuronectiformes"  "Paralichthyidae" "Etropus"           
Seq_33  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Sciaenidae"      "Cynoscion"         
Seq_34  "Eukaryota"  "Chordata" "Actinopteri"    "Labriformes"        "Labridae"        "Tautogolabrus"     
Seq_36  "Eukaryota"  "Chordata" "Actinopteri"    "Anguilliformes"     "Anguillidae"     "Anguilla"          
Seq_38  "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Scombridae"      "Thunnus"           
Seq_40  "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Gasterosteidae"  "Apeltes"           
Seq_44  "Eukaryota"  "Chordata" "Actinopteri"    "Cyprinodontiformes" "Fundulidae"      "Fundulus"          
Seq_50  "Eukaryota"  "Chordata" "Actinopteri"    "Atheriniformes"     "Atherinopsidae"  "Membras"           
Seq_52  "Eukaryota"  "Chordata" "Actinopteri"    "Gadiformes"         "Phycidae"        "Urophycis"         
Seq_54  "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Scombridae"      "Scomber"           
Seq_57  "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Triglidae"       "Prionotus"         
Seq_67  "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Scombridae"      "Thunnus"           
Seq_82  "Eukaryota"  "Chordata" "Actinopteri"    NA                   "Sciaenidae"      "Bairdiella"        
Seq_84  "Eukaryota"  "Chordata" "Actinopteri"    "Gadiformes"         "Gadidae"         "Microgadus"        
Seq_115 "Eukaryota"  "Chordata" "Actinopteri"    "Cyprinodontiformes" "Fundulidae"      "Fundulus"          
Seq_119 "Eukaryota"  "Chordata" "Actinopteri"    "Gadiformes"         "Phycidae"        "Urophycis"         
Seq_139 "Eukaryota"  "Chordata" "Actinopteri"    "Batrachoidiformes"  "Batrachoididae"  "Opsanus"           
Seq_141 "Eukaryota"  "Chordata" "Actinopteri"    "Scombriformes"      "Scombridae"      "Katsuwonus"        
Seq_181 "Eukaryota"  "Chordata" "Actinopteri"    "Tetraodontiformes"  "Tetraodontidae"  "Sphoeroides"       
Seq_231 "Eukaryota"  "Chordata" "Actinopteri"    "Gadiformes"         "Merlucciidae"    "Merluccius"        
Seq_359 "Eukaryota"  "Chordata" "Actinopteri"    "Perciformes"        "Triglidae"       "Prionotus"         
Seq_362 "Eukaryota"  "Chordata" "Chondrichthyes" "Myliobatiformes"    "Myliobatidae"    "Rhinoptera"        
Seq_372 "Eukaryota"  "Chordata" "Chondrichthyes" "Carcharhiniformes"  "Triakidae"       "Mustelus"          
        species                         CommonName                
Seq_1   "Menidia menidia"               "Atlantic silverside"     
Seq_2   "Brevoortia tyrannus"           "Atlantic menhaden"       
Seq_3   "Anchoa mitchilli"              "Bay anchovy"             
Seq_4   "Pomatomus saltatrix"           "Bluefish"                
Seq_5   "Lutjanus griseus"              "Grey snapper"            
Seq_6   "Paralichthys dentatus"         "Summer flounder"         
Seq_7   "Alosa mediocris"               "Hickory shad"            
Seq_9   "Gobiosoma ginsburgi"           "Seaboard goby"           
Seq_10  "Scophthalmus aquosus"          "Windowpane flounder"     
Seq_11  "Centropristis striata"         "Black seabass"           
Seq_12  "Stenotomus chrysops"           "Scup"                    
Seq_15  "Leiostomus xanthurus"          "Spot"                    
Seq_16  "Menticirrhus saxatilis"        "Northern kingfish"       
Seq_17  "Tautoga onitis"                "Tautog"                  
Seq_19  "Myoxocephalus aenaeus"         "Grubby sculpin"          
Seq_20  "Pseudopleuronectes americanus" "Winter flounder"         
Seq_21  "Morone saxatilis"              "Striped bass"            
Seq_22  "Syngnathus fuscus"             "Northern pipefish"       
Seq_30  "Etropus microstomus"           "Smallmouth flounder"     
Seq_33  "Cynoscion regalis"             "Weakfish"                
Seq_34  "Tautogolabrus adspersus"       "Cunner"                  
Seq_36  "Anguilla rostrata"             "American eel"            
Seq_38  "Thunnus obesus"                "Bigeye tuna"             
Seq_40  "Apeltes quadracus"             "Stickleback"             
Seq_44  "Fundulus majalis"              "Striped killifish"       
Seq_50  "Membras martinica"             "Rough silverside"        
Seq_52  "Urophycis floridana"           "Spotted hake"            
Seq_54  "Scomber colias"                "Atlantic chub mackerel"  
Seq_57  "Prionotus carolinus"           "Northern searobin"       
Seq_67  "Thunnus thynnus"               "Atlantic bluefin tuna"   
Seq_82  "Bairdiella chrysoura"          "American silver perch"   
Seq_84  "Microgadus tomcod"             "Atlantic tomcod"         
Seq_115 "Fundulus heteroclitus"         "Mummichog"               
Seq_119 "Urophycis floridana"           "Red hake"                
Seq_139 "Opsanus tau"                   "Oyster toadfish"         
Seq_141 "Katsuwonus pelamis"            "Skipjack tuna"           
Seq_181 "Sphoeroides maculatus"         "Northern puffer"         
Seq_231 "Merluccius bilinearis"         "Silver hake"             
Seq_359 "Prionotus evolans"             "Striped searobin"        
Seq_362 "Rhinoptera bonasus"            "Cownose ray"             
Seq_372 "Mustelus canis"                "Dusky smooth-hound shark"

Bubble plots

Based on my previous scripts with Cariaco Eukaryotic data

# convert ps object to dataframe using phyloseq's psmelt
species_df <- psmelt(speciesGlommed_RA)

# replace zeroes in the table with NA
species_df[species_df == 0] <- NA

# and remove rows with NAs in abundance  (this is so they don't appear as small dots in plot)
species_df <-  filter(species_df, !is.na(Abundance))

Plot by species, scientific name

speciesbubbleplot_eDNA_sciname <- ggplot(species_df, aes(x = Station, y = fct_rev(species), color = Station)) + # the fancy stuff around y (species) helps to present it in reverse order in the plot (from top to btm alphabetically)
  geom_point(aes(size = Abundance, fill = Station), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(0,.25,.5,.75,1), max_size = 6)+
  xlab("")+
  ylab("")+
  labs(size="Relative Abundance")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(Datecode~Bayside, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_eDNA_sciname

Plot by species common name

speciesbubbleplot_eDNA_comname <- ggplot(species_df, aes(x = Station, y = fct_rev(CommonName), color = Station)) + # the fancy stuff around y (CommonName) helps to present it in reverse order in the plot (from top to btm alphabetically)
  geom_point(aes(size = Abundance, fill = Station), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(0,.25,.5,.75,1), max_size = 6)+
  xlab("")+
  ylab("")+
  labs(size="Relative Abundance")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(Datecode~Bayside, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_eDNA_comname

Exportfigures

ggsave(filename = "Figures/speciesbubbleplot_eDNA_sciname.eps", plot = speciesbubbleplot_eDNA_sciname, units = c("in"), width = 7, height = 12, dpi = 300)

ggsave(filename = "Figures/speciesbubbleplot_eDNA_comname.eps", plot = speciesbubbleplot_eDNA_comname, units = c("in"), width = 7, height = 12, dpi = 300)

Bubble plot without Elasmobranchs

The above look good but they include two elasmobranchs, the dusky smooth-hound shark and cownose ray. While these are probably real, the MiFISH primers don’t actually target the elasmobranchs, so we can’t trust this assay to fairly represent these non-target species. Filter out and re-make the bubble plots:

ps_no_elasmo <- subset_taxa(ps, !CommonName == 'Cownose ray')
ps_no_elasmo <- subset_taxa(ps_no_elasmo, !CommonName =='Dusky smooth-hound shark')

ps_ra_no_elasmo <- subset_taxa(ps_ra, !CommonName == 'Cownose ray')
ps_ra_no_elasmo <- subset_taxa(ps_ra_no_elasmo, !CommonName =='Dusky smooth-hound shark')

# and check
speciesGlommed_RA_no_elasmo = tax_glom(ps_ra_no_elasmo, "CommonName")
speciesGlommed_RA_no_elasmo

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 39 taxa and 47 samples ]
sample_data() Sample Data:       [ 47 samples by 12 sample variables ]
tax_table()   Taxonomy Table:    [ 39 taxa by 8 taxonomic ranks ]

tax_table(speciesGlommed_RA_no_elasmo)

Taxonomy Table:     [39 taxa by 8 taxonomic ranks]:
        superkingdom phylum     class         order                family            genus               
Seq_1   "Eukaryota"  "Chordata" "Actinopteri" "Atheriniformes"     "Atherinopsidae"  "Menidia"           
Seq_2   "Eukaryota"  "Chordata" "Actinopteri" "Clupeiformes"       "Clupeidae"       "Brevoortia"        
Seq_3   "Eukaryota"  "Chordata" "Actinopteri" "Clupeiformes"       "Engraulidae"     "Engraulis"         
Seq_4   "Eukaryota"  "Chordata" "Actinopteri" "Scombriformes"      "Pomatomidae"     "Pomatomus"         
Seq_5   "Eukaryota"  "Chordata" "Actinopteri" "Lutjaniformes"      "Lutjanidae"      "Lutjanus"          
Seq_6   "Eukaryota"  "Chordata" "Actinopteri" "Pleuronectiformes"  "Paralichthyidae" "Paralichthys"      
Seq_7   "Eukaryota"  "Chordata" "Actinopteri" "Clupeiformes"       "Clupeidae"       "Alosa"             
Seq_9   "Eukaryota"  "Chordata" "Actinopteri" "Gobiiformes"        "Gobiidae"        "Gobiosoma"         
Seq_10  "Eukaryota"  "Chordata" "Actinopteri" "Pleuronectiformes"  "Scophthalmidae"  "Scophthalmus"      
Seq_11  "Eukaryota"  "Chordata" "Actinopteri" "Perciformes"        "Serranidae"      "Centropristis"     
Seq_12  "Eukaryota"  "Chordata" "Actinopteri" "Spariformes"        "Sparidae"        "Stenotomus"        
Seq_15  "Eukaryota"  "Chordata" "Actinopteri" NA                   "Sciaenidae"      "Leiostomus"        
Seq_16  "Eukaryota"  "Chordata" "Actinopteri" NA                   "Sciaenidae"      "Menticirrhus"      
Seq_17  "Eukaryota"  "Chordata" "Actinopteri" "Labriformes"        "Labridae"        "Tautoga"           
Seq_19  "Eukaryota"  "Chordata" "Actinopteri" "Perciformes"        "Cottidae"        "Myoxocephalus"     
Seq_20  "Eukaryota"  "Chordata" "Actinopteri" "Pleuronectiformes"  "Pleuronectidae"  "Pseudopleuronectes"
Seq_21  "Eukaryota"  "Chordata" "Actinopteri" NA                   "Moronidae"       "Morone"            
Seq_22  "Eukaryota"  "Chordata" "Actinopteri" "Syngnathiformes"    "Syngnathidae"    "Syngnathus"        
Seq_30  "Eukaryota"  "Chordata" "Actinopteri" "Pleuronectiformes"  "Paralichthyidae" "Etropus"           
Seq_33  "Eukaryota"  "Chordata" "Actinopteri" NA                   "Sciaenidae"      "Cynoscion"         
Seq_34  "Eukaryota"  "Chordata" "Actinopteri" "Labriformes"        "Labridae"        "Tautogolabrus"     
Seq_36  "Eukaryota"  "Chordata" "Actinopteri" "Anguilliformes"     "Anguillidae"     "Anguilla"          
Seq_38  "Eukaryota"  "Chordata" "Actinopteri" "Scombriformes"      "Scombridae"      "Thunnus"           
Seq_40  "Eukaryota"  "Chordata" "Actinopteri" "Perciformes"        "Gasterosteidae"  "Apeltes"           
Seq_44  "Eukaryota"  "Chordata" "Actinopteri" "Cyprinodontiformes" "Fundulidae"      "Fundulus"          
Seq_50  "Eukaryota"  "Chordata" "Actinopteri" "Atheriniformes"     "Atherinopsidae"  "Membras"           
Seq_52  "Eukaryota"  "Chordata" "Actinopteri" "Gadiformes"         "Phycidae"        "Urophycis"         
Seq_54  "Eukaryota"  "Chordata" "Actinopteri" "Scombriformes"      "Scombridae"      "Scomber"           
Seq_57  "Eukaryota"  "Chordata" "Actinopteri" "Perciformes"        "Triglidae"       "Prionotus"         
Seq_67  "Eukaryota"  "Chordata" "Actinopteri" "Scombriformes"      "Scombridae"      "Thunnus"           
Seq_82  "Eukaryota"  "Chordata" "Actinopteri" NA                   "Sciaenidae"      "Bairdiella"        
Seq_84  "Eukaryota"  "Chordata" "Actinopteri" "Gadiformes"         "Gadidae"         "Microgadus"        
Seq_115 "Eukaryota"  "Chordata" "Actinopteri" "Cyprinodontiformes" "Fundulidae"      "Fundulus"          
Seq_119 "Eukaryota"  "Chordata" "Actinopteri" "Gadiformes"         "Phycidae"        "Urophycis"         
Seq_139 "Eukaryota"  "Chordata" "Actinopteri" "Batrachoidiformes"  "Batrachoididae"  "Opsanus"           
Seq_141 "Eukaryota"  "Chordata" "Actinopteri" "Scombriformes"      "Scombridae"      "Katsuwonus"        
Seq_181 "Eukaryota"  "Chordata" "Actinopteri" "Tetraodontiformes"  "Tetraodontidae"  "Sphoeroides"       
Seq_231 "Eukaryota"  "Chordata" "Actinopteri" "Gadiformes"         "Merlucciidae"    "Merluccius"        
Seq_359 "Eukaryota"  "Chordata" "Actinopteri" "Perciformes"        "Triglidae"       "Prionotus"         
        species                         CommonName              
Seq_1   "Menidia menidia"               "Atlantic silverside"   
Seq_2   "Brevoortia tyrannus"           "Atlantic menhaden"     
Seq_3   "Anchoa mitchilli"              "Bay anchovy"           
Seq_4   "Pomatomus saltatrix"           "Bluefish"              
Seq_5   "Lutjanus griseus"              "Grey snapper"          
Seq_6   "Paralichthys dentatus"         "Summer flounder"       
Seq_7   "Alosa mediocris"               "Hickory shad"          
Seq_9   "Gobiosoma ginsburgi"           "Seaboard goby"         
Seq_10  "Scophthalmus aquosus"          "Windowpane flounder"   
Seq_11  "Centropristis striata"         "Black seabass"         
Seq_12  "Stenotomus chrysops"           "Scup"                  
Seq_15  "Leiostomus xanthurus"          "Spot"                  
Seq_16  "Menticirrhus saxatilis"        "Northern kingfish"     
Seq_17  "Tautoga onitis"                "Tautog"                
Seq_19  "Myoxocephalus aenaeus"         "Grubby sculpin"        
Seq_20  "Pseudopleuronectes americanus" "Winter flounder"       
Seq_21  "Morone saxatilis"              "Striped bass"          
Seq_22  "Syngnathus fuscus"             "Northern pipefish"     
Seq_30  "Etropus microstomus"           "Smallmouth flounder"   
Seq_33  "Cynoscion regalis"             "Weakfish"              
Seq_34  "Tautogolabrus adspersus"       "Cunner"                
Seq_36  "Anguilla rostrata"             "American eel"          
Seq_38  "Thunnus obesus"                "Bigeye tuna"           
Seq_40  "Apeltes quadracus"             "Stickleback"           
Seq_44  "Fundulus majalis"              "Striped killifish"     
Seq_50  "Membras martinica"             "Rough silverside"      
Seq_52  "Urophycis floridana"           "Spotted hake"          
Seq_54  "Scomber colias"                "Atlantic chub mackerel"
Seq_57  "Prionotus carolinus"           "Northern searobin"     
Seq_67  "Thunnus thynnus"               "Atlantic bluefin tuna" 
Seq_82  "Bairdiella chrysoura"          "American silver perch" 
Seq_84  "Microgadus tomcod"             "Atlantic tomcod"       
Seq_115 "Fundulus heteroclitus"         "Mummichog"             
Seq_119 "Urophycis floridana"           "Red hake"              
Seq_139 "Opsanus tau"                   "Oyster toadfish"       
Seq_141 "Katsuwonus pelamis"            "Skipjack tuna"         
Seq_181 "Sphoeroides maculatus"         "Northern puffer"       
Seq_231 "Merluccius bilinearis"         "Silver hake"           
Seq_359 "Prionotus evolans"             "Striped searobin"      

Remake bubble plots. First melt for tidyverse format

# convert ps object to dataframe using phyloseq's psmelt
species_df_no_elasmo <- psmelt(speciesGlommed_RA_no_elasmo)

# replace zeroes in the table with NA
species_df_no_elasmo[species_df_no_elasmo == 0] <- NA

# and remove rows with NAs in abundance  (this is so they don't appear as small dots in plot)
species_df_no_elasmo <-  filter(species_df_no_elasmo, !is.na(Abundance))

Plot by species, scientific name

speciesbubbleplot_eDNA_sciname_no_elasmo <- ggplot(species_df_no_elasmo, aes(x = Station, y = fct_rev(species), color = Station)) + # the fancy stuff around y (species) helps to present it in reverse order in the plot (from top to btm alphabetically)
  geom_point(aes(size = Abundance, fill = Station), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(0,.25,.5,.75,1), max_size = 6)+
  xlab("")+
  ylab("")+
  labs(size="Relative Abundance")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(Datecode~Bayside, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_eDNA_sciname_no_elasmo

Plot by species common name

speciesbubbleplot_eDNA_comname_no_elasmo <- ggplot(species_df_no_elasmo, aes(x = Station, y = fct_rev(CommonName), color = Station)) + # the fancy stuff around y (CommonName) helps to present it in reverse order in the plot (from top to btm alphabetically)
  geom_point(aes(size = Abundance, fill = Station), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(0,.25,.5,.75,1), max_size = 6)+
  xlab("")+
  ylab("")+
  labs(size="Relative Abundance")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(Datecode~Bayside, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_eDNA_comname_no_elasmo

Exportfigures

ggsave(filename = "Figures/speciesbubbleplot_eDNA_sciname_no_elasmo.eps", plot = speciesbubbleplot_eDNA_sciname_no_elasmo, units = c("in"), width = 7, height = 12, dpi = 300)

ggsave(filename = "Figures/speciesbubbleplot_eDNA_comname_no_elasmo.eps", plot = speciesbubbleplot_eDNA_comname_no_elasmo, units = c("in"), width = 7, height = 12, dpi = 300)

Import and prepare the data from trawls

Import Trawl Count Data

# import 4th sheet from  Excel file which contains morphometric data for each individual collected for every date
trawl_master <- read_excel("Trawls MASTER 2020 _mod_ES.xlsx",4)

# and import 6th sheet which is station info
stations <- read_excel("Trawls MASTER 2020 _mod_ES.xlsx",6)

# and import shedding factor- an index determined by Sara that indicates how much the species sheds when handled (and therefore how likely it is to shed cells in water)
sheddingfactor <- read_excel("Allometric correction_mod.xlsx",5)

# Group station name and shedding factor into trawl_master table
trawl_master <- left_join(trawl_master, stations, by = "STATION_NO")
trawl_master <- left_join(trawl_master, sheddingfactor, by = "COMMONNAME")

trawl_master

Import station/ trawl information

station_data <- read_excel("Trawls MASTER 2020 _mod_ES.xlsx",1)
station_data

# Filter to only include DATECODE, Station_NO, Trawl_Min
station_data <- station_data %>% select(DATECODE, STATION_NO, Trawl_Min)
station_data

Combine station information to trawl_master in order to have the duration of each trawl (for calculating CPUE)

trawl_master <- left_join(trawl_master, station_data, by = c("DATECODE", "STATION_NO"))
trawl_master

Make a count table from trawl_master, grouping by date and location, summing the counts for every unique species, and summing the total length for each trawl/ species

trawl_counts <- trawl_master %>%
  group_by(DATECODE, STATION_NA, STATION_NO, Trawl_Min, BAYSIDE, CommonName, SheddingFactor) %>%
  summarize(TotalLength = sum(TL_CM))

`summarise()` has grouped output by 'DATECODE', 'STATION_NA', 'STATION_NO', 'Trawl_Min', 'BAYSIDE', 'CommonName'. You can override using the `.groups` argument.

counts <- trawl_master %>%
  group_by(DATECODE, STATION_NA, STATION_NO, CommonName) %>%
  tally(name = "count")

trawl_counts <- left_join(trawl_counts, counts, by = c ("DATECODE", "STATION_NA", "STATION_NO", "CommonName"))
trawl_counts

Calculate CPUE and put in new column. CPUE is the count divided by trawl time (in minutes)

trawl_counts <- trawl_counts %>%
  mutate (CPUE = count / Trawl_Min)
trawl_counts

Calculate the metric that Sara came up with: sum(total length) * shedding factor. This is a correction of the abundance that takes into account the sums of length of each fish for each date/trawl and multiplies by a factor determined by how much they shed.

trawl_counts <- trawl_counts %>%
  mutate ("TLxSF" = TotalLength*SheddingFactor)
trawl_counts

Then also divide the TotalLength and SumTL*SF by the trawl time in order to account for effort (similar to CPUE)

trawl_counts <- trawl_counts %>%
  mutate ("TLPUE" = TotalLength/Trawl_Min)

trawl_counts <- trawl_counts %>%
  mutate ("TLxSF.PUE" = TLxSF/Trawl_Min)

trawl_counts

Remove 09/16/20 since there is no equivalent eDNA from that date. Also remove station 13 since no equivalent eDNA samples were collected there

trawl_counts <- trawl_counts %>%
  filter(DATECODE != "20200916")

trawl_counts <- trawl_counts %>%
  filter(STATION_NO != "13")

Abundance plots Trawls

Bubble plots of Counts

speciesbubbleplot_trawl_comname <- ggplot(trawl_counts, aes(x = STATION_NA, y = fct_rev(CommonName), color = STATION_NA)) + 
  geom_point(aes(size = log10(count), fill = STATION_NA), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(log10(1), log10(2), log10(5), log10(10), log10(25), log10(100)), max_size = 6, labels = c("1","2","5","10","25","100"))+
  xlab("")+
  ylab("")+
  labs(size="Abundance", fill = "Station")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(DATECODE~BAYSIDE, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_trawl_comname

Export figure

ggsave(filename = "Figures/speciesbubbleplot_trawl_abundance_comname.eps", plot = speciesbubbleplot_trawl_comname, units = c("in"), width = 6.75, height = 13, dpi = 300)

Bubble plots of CPUE

speciesbubbleplot_trawl_CPUE_comname <- ggplot(trawl_counts, aes(x = STATION_NA, y = fct_rev(CommonName), color = STATION_NA)) + 
  geom_point(aes(size = log10(CPUE), fill = STATION_NA), color = "black", pch = 21)+
  scale_size(range = c(1,15)) +
  scale_size_area(breaks = c(log10(1), log10(2), log10(5), log10(10), log10(25), log10(100)), max_size = 6, labels = c("1","2","5","10","25","100"))+
  xlab("")+
  ylab("")+
  labs(size="CPUE", fill = "Station")+
  theme_bw() +
  scale_fill_brewer(palette="Paired") +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  facet_grid(DATECODE~BAYSIDE, scales = "free", space = "free", drop= TRUE)

Scale for 'size' is already present. Adding another scale for 'size', which will replace the existing scale.

speciesbubbleplot_trawl_CPUE_comname

Looks good! Similar to “counts” figure but some adjustments that normalized for trawling time.

Export figure

ggsave(filename = "Figures/speciesbubbleplot_trawl_CPUE_comname.eps", plot = speciesbubbleplot_trawl_CPUE_comname, units = c("in"), width = 6.75, height = 13, dpi = 300)

Compare Trawl and eDNA

Prepare the data

First, remove the species from the trawls that are not targeted in the eDNA assay (invertebrates and elasmobranchs)

# import a list of the "OK" species that are targetted by MiFISh primers
mifish_spp <- read_excel("Trawl CPUE no elasmobranch_mod.xlsx",2)
mifish_spp

# filter rows from trawl_counts if the spp name doesn't match the MiFISh list
trawl_counts <- right_join(trawl_counts, mifish_spp, by = "CommonName")
trawl_counts

Then filter out stations from trawl data that were removed samples from eDNA analysis because of poor sequencing effort.

# Grab the eDNA sample IDs that remained after filtering

There were 11 warnings (use warnings() to see them)

sampleIDs <- as_data_frame(sample_data(ps_no_elasmo)) %>% 
  select(SampleID, Datecode, Station)

`as_data_frame()` was deprecated in tibble 2.0.0.
Please use `as_tibble()` instead.
The signature and semantics have changed, see `?as_tibble`.Setting class(x) to multiple strings ("tbl_df", "tbl", ...); result will no longer be an S4 object

# Filter trawl_counts to only include those same samples
trawl_counts <- inner_join(trawl_counts, sampleIDs, by = c("DATECODE" = "Datecode", "STATION_NA" = "Station"))
trawl_counts

Make abundance table of each species across whole study

# sum hits across all dates in trawl
trawl_uniques <- trawl_counts %>%
  group_by(DATECODE, CommonName) %>%
  summarise(Trawl_Count = sum(count, na.rm=TRUE), Trawl_CPUE = sum(CPUE, na.rm = TRUE), Trawl_TLPUE = sum(TLPUE, na.rm = TRUE), Trawl_Allometric_Shedding = sum(TLxSF.PUE, na.rm = TRUE))

`summarise()` has grouped output by 'DATECODE'. You can override using the `.groups` argument.

trawl_uniques

# sum hits across all dates in eDNA
eDNA_uniques <- species_df_no_elasmo%>%
  group_by(Datecode, CommonName) %>%
  summarise(eDNA_RelAbun = sum(Abundance, na.rm=TRUE))

`summarise()` has grouped output by 'Datecode'. You can override using the `.groups` argument.

eDNA_uniques

# Combine into one dataframe
trawl_eDNA_abun_table <- full_join(trawl_uniques, eDNA_uniques, by=c("CommonName" = "CommonName", "DATECODE" = "Datecode"))

trawl_eDNA_abun_table

Species Richness

Count unique species across all stations, grouped by date, for each method, trawl& eDNA (use filtered trawl data so only comparing MiFISh spp to MiFISh spp).

Count total number of species from each method for each date

eDNA_richness <- tally(eDNA_uniques, name = "eDNA")
trawl_richness <- tally(trawl_uniques, name = "trawl")

speciesrichness <- full_join(eDNA_richness, trawl_richness, c("Datecode" = "DATECODE"))
speciesrichness <- pivot_longer(speciesrichness, !Datecode, names_to = "Method", values_to = "Richness")

speciesrichness$Datecode <- ymd(speciesrichness$Datecode) # convert to date format (better for plotting)

speciesrichness

Plot side-by-side

species_richness_plot <- ggplot(speciesrichness, aes(x =Datecode, y = Richness)) +
  geom_line(aes(color = Method), size = 3) +
  theme_bw() +
  xlab("") +
  ylab("Species Richness")

species_richness_plot

# export plot
ggsave(filename = "Figures/species_richness_plot.eps", plot = species_richness_plot, units = c("in"), width = 4, height = 3, dpi = 300)

Sum total number of species across all dates/ stations for entire study

species_sums_abun_table <- trawl_eDNA_abun_table %>%
  group_by(CommonName) %>%
  summarise(CPUE = sum(Trawl_CPUE, na.rm = TRUE), 
            "Total Length (TL) PUE" = sum(Trawl_TLPUE, na.rm = TRUE), 
            "TL * Shedding Factor PUE" = sum(Trawl_Allometric_Shedding, na.rm = TRUE), 
            eDNA = sum(eDNA_RelAbun, na.rm=TRUE)) %>%
  pivot_longer(!CommonName, names_to = "Method", values_to = "Abundance")
  
# turn zeroes to NA so they don't plot 
species_sums_abun_table <- na_if(species_sums_abun_table,0)

species_sums_abun_table

Species tally across whole study

For each species, plot side-by-side comparison of abundance (summed over whole study) using each method

# First create a custom color scale to make this pretty
myColors <- colorRampPalette(brewer.pal(11,"Spectral"))(40)
names(myColors) <- levels(unique(species_sums_abun_table$CommonName))
colScale <- scale_colour_manual(name = "CommonName",values = myColors)

species_abun_sum_plot <- ggplot(species_sums_abun_table, aes(x = Abundance, y = reorder(CommonName, Abundance, function(x){sum(x,na.rm = TRUE)}), color = CommonName)) +
  geom_point(size = 5) +
  facet_wrap(~factor(Method, levels = c('CPUE','Total Length (TL) PUE','TL * Shedding Factor PUE','eDNA')), scales = "free_x", ncol = 4) +
  theme_bw() +
  xlab("Abundance") +
  ylab("") + 
  colScale +
  theme(legend.position = "none")

species_abun_sum_plot

Export plot

ggsave(filename = "Figures/species_abun_sum_plot.eps", plot = species_abun_sum_plot, units = c("in"), width = 10, height = 6, dpi = 300)

Exploratory Analyses

Ordinations on eDNA

I will try PCoA, PCA (the Euclidean PCoA) and NMDS ordinations in combination with different tranformations and distance metrics in order to see which explain the most variance in the dataset.

• NOTE- see this discussion and this paper on why CCA should not be used with CLR-transformed compositional data to explore correlations.

PCA

PCA is essentially a type of PCoA using the Euclidean distance matrix as input. When combined with a log-ratio transformation of the count table, this is deemed appropriate for compositional datasets. It is also recommended as a first step in exploratory analyses of sequencinging datasets.

First do a CLR, centered log ratio transformation of the absolute abundance data (after filtering), as suggested by Gloor et al. 2017

# Estimate covariance matrix for CLR-transformed ASV table
clr_asv_table_ps <- data.frame(compositions::clr(otu_table(ps_no_elasmo)))

Generate the PCA and visualize axes

# Generate a Principle Component Analysis (PCA) and evaluated based on the eigen decomposition from sample covariance matrix. 
lograt_pca <- prcomp(clr_asv_table_ps) 
# NOTE- this is equivalent to first making a Euclidean distance matrix using the CLR data table and then running a PCoA. A Euclidean distance matrix of a log-transformed data table = an Aitchison distance matrix. So this is equivalent to the compositional methods listed in Gloor et al.

# Visual representation with a screeplot
lograt_variances <- as.data.frame(lograt_pca$sdev^2/sum(lograt_pca$sdev^2)) %>% #Extract axes
  # Format to plot
  select(PercVar = 'lograt_pca$sdev^2/sum(lograt_pca$sdev^2)') %>% 
  rownames_to_column(var = "PCaxis") %>% 
  data.frame
head(lograt_variances)

# Plot screeplot
ggplot(lograt_variances, aes(x = as.numeric(PCaxis), y = PercVar)) + 
  geom_bar(stat = "identity", fill = "grey", color = "black") +
  theme_minimal() +
  theme(axis.title = element_text(color = "black", face = "bold", size = 10),
        axis.text.y = element_text(color = "black", face = "bold"),
        axis.text.x = element_blank()) +
  labs(x = "PC axis", y = "% Variance", title = "Log-Ratio PCA Screeplot, CLR Tranformation")

Total variance explained by first three axes= 15.8 + 10.7 + 10.1 = 36.6%. Since the second and third axes are similar, plot in 3D with 3 axes.

Visualize the PCA-

# Extract variances from the clr pca
pca_lograt_frame <- data.frame(lograt_pca$x) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
pca_lograt_frame <- left_join(pca_lograt_frame, metadata, by = "SampleID")
head(pca_lograt_frame)

# Select eigenvalues from dataframe, round to 4 places and multiply by 100 for plotting. These will be the axes for the 3-D plot
eigenvalues<-round(lograt_variances[,2], digits = 4)*100

# Plotly - 3-D
pca_lograt <- plot_ly(pca_lograt_frame, type='scatter3d', mode='markers',
        x=~PC1,y=~PC2,z=~PC3,colors=~brewer.pal(11,'Paired'),
        color=~Station, symbols = c('circle','diamond'), symbol=~Bayside)%>%
  layout(font=list(size=12),
         title='CLR-Euclidean PCA',
         scene=list(xaxis=list(title=paste0('Co 2 ',eigenvalues[2],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    yaxis=list(title=paste0('Co 3 ',eigenvalues[3],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    zaxis=list(title=paste0('Co 1 ',eigenvalues[1],'%'),
                               showticklabels=FALSE,zerolinecolor='black')))
# pca_lograt

# save in "Embedded_figures" directory so that it can be hosted at Github and embedded in this notebook
withr::with_dir('Embedded_figures', htmlwidgets::saveWidget(as_widget(pca_lograt), file="pca_lograt.html", selfcontained = F))

`arrange_()` was deprecated in dplyr 0.7.0.
Please use `arrange()` instead.
See vignette('programming') for more help

Summary The CLR-Euclidean PCA reveals there is some separation according to East vs West. The first 3 PCs only explain ~36% of the variance so keep going with different ordinations to see if there is a better representation

PCoA Jaccard

The more traditional approach to ordinations is to do a PCoA on a distance matrix such as Bray-Curtis, Jaccard, or Unifrac. When combined with a transformation, they become more appropriate for NGS data. One such common transformation is the Hellinger transformation.

The different distance matrices also tell you a few different things about the dataset so I will run try different one to try to see if I can tease those out.

Before calculating any distance matrix, do a transformation of the filtered count table. Hellinger transformation is the square root of the relative abundance, so calculate it based on the ps_ra object:

ps_hellinger <- transform_sample_counts(ps_ra_no_elasmo, function(x){sqrt(x)})

First, Jaccard, which builds the distance matrix based on presence/absence between samples. It does not take into account relative abundance of the taxa. Therefore this functions well for determining differences driven by rare taxa, which are weighed the same as abundant taxa.

jac_dmat<-vegdist(otu_table(ps_hellinger),method="jaccard") # Jaccard dist metric
pcoa_jac<-ape::pcoa(jac_dmat) # perform PCoA

# Extract variances from pcoa, from jaccard calculated dist. metric
jac_variances <- data.frame(pcoa_jac$values$Relative_eig) %>% 
  select(PercVar = 'pcoa_jac.values.Relative_eig') %>% 
  rownames_to_column(var = "PCaxis") %>% 
  data.frame
head(jac_variances)

# Make a screeplot
ggplot(jac_variances, aes(x = as.numeric(PCaxis), y = PercVar)) + 
  geom_bar(stat = "identity", fill = "grey", color = "black") +
  theme_minimal() +
  theme(axis.title = element_text(color = "black", face = "bold", size = 10),
        axis.text.y = element_text(color = "black", face = "bold"),
        axis.text.x = element_blank()) +
  labs(x = "PC axis", y = "% Variance", title = "Jaccard PCoA Screeplot")

The first two axes (19.0 + 9.7 = 28.7) are OK. But plot the first 3 axes since the 2nd and 3rd explain a similar amount of variance, (19.0 + 9.7 + 8.4 = 37.1% total variance explained)

Plot in 3D with Plotly

# Extract variances from the jaccard pcoa
pcoa_jac_df <- data.frame(pcoa_jac$vectors) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
pcoa_jac_df <- left_join(pcoa_jac_df, metadata, by = "SampleID")
head(pcoa_jac_df)

# Select eigenvalues from dataframe, round to 4 places and multiply by 100 for plotting. These will be the axes for the 3-D plot
eigenvalues<-round(jac_variances[,2], digits = 4)*100

# Plotly - 3-D
pcoa_jaccard <- plot_ly(pcoa_jac_df, type='scatter3d', mode='markers',
        x=~Axis.2,y=~Axis.3,z=~Axis.1,colors=~brewer.pal(11,'Paired'),
        color=~Station, symbols = c('circle','diamond'), symbol=~Bayside)%>%
  layout(font=list(size=12),
         title='PCoA Jaccard Distance',
         scene=list(xaxis=list(title=paste0('Co 2 ',eigenvalues[2],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    yaxis=list(title=paste0('Co 3 ',eigenvalues[3],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    zaxis=list(title=paste0('Co 1 ',eigenvalues[1],'%'),
                               showticklabels=FALSE,zerolinecolor='black')))
# pcoa_jaccard

# save figure in "Embedded_figures" directory so that it can be hosted at Github and embedded in this notebook
withr::with_dir('Embedded_figures', htmlwidgets::saveWidget(as_widget(pcoa_jaccard), file="pcoa_jaccard.html", selfcontained = F))

The Jaccard-PCoA shows some separation along axis 2 and axis 3 in East vs West differences. Very similar % variance explained to the PCA.

PCoA Bray Curtis

Next, try a Bray-Curtis distance matrix with PCoA, which builds the distance matrix based on presence/absence between samples and relative abundance differences. This ordination will represent well the differences in samples that are driven by taxa with high relative abundances.

NOTE: I need to use a correction here for negative eigenvalues. Read more here

bray_dmat<-vegdist(otu_table(ps_hellinger),method="bray") # Bray-Curtis dist metric
pcoa_bray<-ape::pcoa(bray_dmat) # perform PCoA in ape. But getting negative eigenvalues, so need to add correction. wcmdscale from base R also performs PCoA and can add cailliez correction
pcoa_bray <- wcmdscale(bray_dmat, eig = TRUE, add = "cailliez")

# check out summary of PCoA
eigenvals(pcoa_bray) %>%
  summary() -> ev
ev

Importance of components:
                        [,1]   [,2]    [,3]    [,4]    [,5]    [,6]    [,7]    [,8]    [,9]   [,10]   [,11]   [,12]
Eigenvalue            6.3479 3.3005 2.85957 1.62805 1.33439 1.24855 1.00938 0.90346 0.87311 0.77992 0.71218 0.65613
Proportion Explained  0.2112 0.1098 0.09512 0.05416 0.04439 0.04153 0.03358 0.03005 0.02904 0.02594 0.02369 0.02183
Cumulative Proportion 0.2112 0.3210 0.41608 0.47023 0.51462 0.55615 0.58973 0.61978 0.64883 0.67477 0.69846 0.72029
                        [,13]   [,14]  [,15]   [,16]   [,17]   [,18]  [,19]   [,20]   [,21]   [,22]   [,23]
Eigenvalue            0.60610 0.54826 0.4989 0.44174 0.40567 0.39186 0.3667 0.34891 0.33706 0.33146 0.30199
Proportion Explained  0.02016 0.01824 0.0166 0.01469 0.01349 0.01304 0.0122 0.01161 0.01121 0.01103 0.01005
Cumulative Proportion 0.74045 0.75869 0.7753 0.78998 0.80347 0.81651 0.8287 0.84031 0.85152 0.86255 0.87259
                         [,24]    [,25]    [,26]    [,27]    [,28]    [,29]    [,30]    [,31]    [,32]    [,33]
Eigenvalue            0.284949 0.268734 0.255852 0.247955 0.239199 0.225418 0.217687 0.198673 0.194406 0.186256
Proportion Explained  0.009479 0.008939 0.008511 0.008248 0.007957 0.007498 0.007241 0.006609 0.006467 0.006196
Cumulative Proportion 0.882071 0.891010 0.899521 0.907769 0.915726 0.923224 0.930465 0.937074 0.943541 0.949737
                         [,34]    [,35]    [,36]    [,37]    [,38]    [,39]   [,40]   [,41]    [,42]    [,43]
Eigenvalue            0.166417 0.156276 0.152618 0.150887 0.139347 0.133210 0.12775 0.12414 0.110383 0.106401
Proportion Explained  0.005536 0.005198 0.005077 0.005019 0.004635 0.004431 0.00425 0.00413 0.003672 0.003539
Cumulative Proportion 0.955273 0.960471 0.965548 0.970567 0.975202 0.979634 0.98388 0.98801 0.991685 0.995224
                         [,44]    [,45]
Eigenvalue            0.085699 0.057876
Proportion Explained  0.002851 0.001925
Cumulative Proportion 0.998075 1.000000

# extract variances and put in tibble
bray_variances <- NULL
for (i in 1:length(eigenvals(pcoa_bray))){
  bray_variances[i] <- eigenvals(pcoa_bray)[i]/sum(eigenvals(pcoa_bray))
}

# Extract variances from pcoa, from calculated dist. metric
bray_variances <- tibble(round(bray_variances,3)) %>%
  select(PercVar = 'round(bray_variances, 3)') %>%
  rownames_to_column(var = "PCaxis") %>%
  data.frame
head(bray_variances)

# Make a screeplot
ggplot(bray_variances, aes(x = as.numeric(PCaxis), y = PercVar)) + 
  geom_bar(stat = "identity", fill = "grey", color = "black") +
  theme_minimal() +
  theme(axis.title = element_text(color = "black", face = "bold", size = 10),
        axis.text.y = element_text(color = "black", face = "bold"),
        axis.text.x = element_blank()) +
  labs(x = "PC axis", y = "% Variance", title = "Bray-Curtis PCoA Screeplot")

The first two axes (21.1 + 11.0) are pretty good again but I am still going to experiment in the plot with the 3rd axis since it is similar to the second (9.5%; total variance explained = 41.6%)

Plot in 3D with Plotly

# Extract variances from the pcoa
pcoa_bray_df <- data.frame(pcoa_bray$points) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
pcoa_bray_df <- left_join(pcoa_bray_df, metadata, by = "SampleID")
head(pcoa_bray_df)

# Select eigenvalues from dataframe, round to 4 places and multiply by 100 for plotting. These will be the axes for the 3-D plot
eigenvalues<-round(bray_variances[,2], digits = 4)*100

# Plotly - 3-D
pcoa_bray <- plot_ly(pcoa_bray_df, type='scatter3d', mode='markers', 
                     x=~Dim2, y=~Dim3, z=~Dim1, colors=~brewer.pal(11,'Paired'),
        color=~Station, symbols = c('circle','diamond'), symbol=~Bayside)%>%  
  layout(font=list(size=12),
         title='PCoA Bray-Curtis Distance',
         scene=list(xaxis=list(title=paste0('Co 2 ',eigenvalues[2],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    yaxis=list(title=paste0('Co 3 ',eigenvalues[3],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    zaxis=list(title=paste0('Co 1 ',eigenvalues[1],'%'),
                               showticklabels=FALSE,zerolinecolor='black')))
# pcoa_bray

# save in "Embedded_figures" directory so that it can be hosted at Github and embedded in this notebook
withr::with_dir('Embedded_figures', htmlwidgets::saveWidget(as_widget(pcoa_bray), file="pcoa_bray.html", selfcontained = F))

These results along axes 1, 2, and 3 are similar to Jaccard, but there is more separation along axis 2, indicating that incorporating the differences in abundance helps explain more variance in the dataset. Total variance explained is highest so far.

NMDS Aitchison

Lastly, try a non-metric dimensional scaling ordination. PCA/PCoA are metric and attempt to rotate axes to fit the distance matrix distribution. An NMDS represents the data in 2-axes, by constraining the distribution of the points. Similar to above, this can be combined with different pre-treatment of the data.

First try the compositional approach, an NMDS on CLR-tranformed data using the Euclidean distances (aka Aitchison distance)

euc_dmat<-dist(clr_asv_table_ps, method = "euclidean") # Build the Aitchison distance matrix
euc_nmds <- metaMDS(euc_dmat, k=2, autotransform=FALSE) # Run the ordination

Run 0 stress 0.2095936 
Run 1 stress 0.2315382 
Run 2 stress 0.2255976 
Run 3 stress 0.2337087 
Run 4 stress 0.2324951 
Run 5 stress 0.2097198 
... Procrustes: rmse 0.01219292  max resid 0.04653278 
Run 6 stress 0.2290337 
Run 7 stress 0.218419 
Run 8 stress 0.2446202 
Run 9 stress 0.2389798 
Run 10 stress 0.2095936 
... Procrustes: rmse 7.474957e-05  max resid 0.0001930906 
... Similar to previous best
Run 11 stress 0.2198137 
Run 12 stress 0.2094383 
... New best solution
... Procrustes: rmse 0.01330652  max resid 0.05641412 
Run 13 stress 0.2139941 
Run 14 stress 0.2097196 
... Procrustes: rmse 0.01534702  max resid 0.059826 
Run 15 stress 0.2096343 
... Procrustes: rmse 0.009354549  max resid 0.05365267 
Run 16 stress 0.2122582 
Run 17 stress 0.2238324 
Run 18 stress 0.2133316 
Run 19 stress 0.2096473 
... Procrustes: rmse 0.01707295  max resid 0.0582493 
Run 20 stress 0.2094392 
... Procrustes: rmse 0.0004828362  max resid 0.001297321 
... Similar to previous best
*** Solution reached

euc_nmds$stress #Check the stress. Less than 0.1 is good. Less than 0.05 is better. This will be different each time, since it is iteratively finding a unique solution each time (although the should look similar)

[1] 0.2094383

# Extract points from nmds and merge into data frame with metadata 
euc_nmds_df <- data.frame(euc_nmds$points) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
euc_nmds_df <- left_join(euc_nmds_df, metadata, by = "SampleID")
head(euc_nmds_df)

## Plotting euclidean distance NMDS
nmds_aitch <- ggplot(euc_nmds_df,aes(x = MDS1, y = MDS2, color = Station, shape = Bayside)) +
  geom_point(size = 4) +
  scale_color_brewer(palette="Paired") +
  theme_bw() +
  labs(x = "NMDS 1", y = "NMDS 2", title = paste0('Aitchison Distance NMDS, Stress = ', round(euc_nmds$stress,2))) +
  coord_fixed(ratio = 1)

nmds_aitch

ggsave("figures/nmds_aitch.eps",nmds_aitch, width = 7, height = 5, units = c("in"))

The above has a relatively high stress (>0.2) so should be interpreted with caution. But it does show some separation East vs West along NMDS 1.

NMDS Jacaard

Next try a Jaccard NMDS, which will represent differences in presence/absence among samples, emphasizing both abundant and rare taxa the same

jac_nmds <- metaMDS(jac_dmat, k=2, autotransform=FALSE) # Run the ordination. Distance matrix was already calculated above

Run 0 stress 0.1625677 
Run 1 stress 0.157417 
... New best solution
... Procrustes: rmse 0.09015074  max resid 0.305869 
Run 2 stress 0.1496705 
... New best solution
... Procrustes: rmse 0.06086793  max resid 0.3331155 
Run 3 stress 0.1498109 
... Procrustes: rmse 0.01164913  max resid 0.06847067 
Run 4 stress 0.1496703 
... New best solution
... Procrustes: rmse 0.000221713  max resid 0.001249097 
... Similar to previous best
Run 5 stress 0.1496711 
... Procrustes: rmse 0.0004789722  max resid 0.002884629 
... Similar to previous best
Run 6 stress 0.1730246 
Run 7 stress 0.1574159 
Run 8 stress 0.1578544 
Run 9 stress 0.1756318 
Run 10 stress 0.1508383 
Run 11 stress 0.1929949 
Run 12 stress 0.150838 
Run 13 stress 0.1496705 
... Procrustes: rmse 0.0001673464  max resid 0.0009287265 
... Similar to previous best
Run 14 stress 0.1652703 
Run 15 stress 0.1496707 
... Procrustes: rmse 0.0003465613  max resid 0.002078341 
... Similar to previous best
Run 16 stress 0.1704111 
Run 17 stress 0.1697825 
Run 18 stress 0.1511936 
Run 19 stress 0.1812576 
Run 20 stress 0.1573221 
*** Solution reached

jac_nmds$stress #Check the stress. Less than 0.1 is good. Less than 0.5 is better. This will be different each time, since it is iteratively finding a unique solution each time (although the should look similar)

[1] 0.1496703

# Extract points from nmds and merge into data frame with metadata 
jac_nmds_df <- data.frame(jac_nmds$points) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
jac_nmds_df <- left_join(jac_nmds_df, metadata, by = "SampleID")
head(jac_nmds_df)

## Plotting euclidean distance NMDS
nmds_jaccard <- ggplot(jac_nmds_df,aes(x = MDS1, y = MDS2, color = Station, shape = Bayside)) +
  geom_point(size = 4) +
  scale_color_brewer(palette="Paired") +
  theme_bw() +
  labs(x = "NMDS 1", y = "NMDS 2", title = paste0('Jaccard Distance NMDS, Stress = ', round(jac_nmds$stress,2))) +
  coord_fixed(ratio = 1)

nmds_jaccard

ggsave("figures/nmds_jaccard.eps",nmds_jaccard, width = 7, height = 5, units = c("in"))

This is still a moderately high stress (>0.1) so should be interpreted with caution. Similar to Aitchison-distance nMDS but there is a little more separation of East vs West on NMDS 2 axis.

NMDS Bray Curtis

Next try a Bray-Curis NMDS, which will represent differences in presence/absence among samples and relative abundance, thus emphasizing impacts of highly abundant taxa.

bray_nmds <- metaMDS(bray_dmat, k=2, autotransform=FALSE) # Run the ordination. Distance matrix was already calculated above

Run 0 stress 0.1628464 
Run 1 stress 0.1889242 
Run 2 stress 0.1496308 
... New best solution
... Procrustes: rmse 0.09264092  max resid 0.3004183 
Run 3 stress 0.1495058 
... New best solution
... Procrustes: rmse 0.01213895  max resid 0.07319522 
Run 4 stress 0.1495058 
... Procrustes: rmse 0.0004547352  max resid 0.002380273 
... Similar to previous best
Run 5 stress 0.1940448 
Run 6 stress 0.1496305 
... Procrustes: rmse 0.0121683  max resid 0.07313183 
Run 7 stress 0.1495062 
... Procrustes: rmse 0.0001653641  max resid 0.0007814818 
... Similar to previous best
Run 8 stress 0.1498105 
... Procrustes: rmse 0.05226558  max resid 0.3267871 
Run 9 stress 0.1574159 
Run 10 stress 0.1657586 
Run 11 stress 0.1634139 
Run 12 stress 0.1634788 
Run 13 stress 0.1496717 
... Procrustes: rmse 0.05082706  max resid 0.327221 
Run 14 stress 0.1727482 
Run 15 stress 0.1796212 
Run 16 stress 0.1508386 
Run 17 stress 0.1641238 
Run 18 stress 0.1512002 
Run 19 stress 0.1625682 
Run 20 stress 0.1717659 
*** Solution reached

bray_nmds$stress #Check the stress. Less than 0.1 is good. Less than 0.5 is better. This will be different each time, since it is iteratively finding a unique solution each time (although the should look similar)

[1] 0.1495058

# Extract points from nmds and merge into data frame with metadata 
bray_nmds_df <- data.frame(bray_nmds$points) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
bray_nmds_df <- left_join(bray_nmds_df, metadata, by = "SampleID")
head(bray_nmds_df)

## Plotting euclidean distance NMDS
nmds_bray <- ggplot(bray_nmds_df,aes(x = MDS1, y = MDS2, color = Station, shape = Bayside)) +
  geom_point(size = 4) +
  scale_color_brewer(palette="Paired") +
  theme_bw() +
  labs(x = "NMDS 1", y = "NMDS 2", title = paste0('Bray-Curtis Distance NMDS, Stress = ', round(bray_nmds$stress,2))) +
  coord_fixed(ratio = 1)

nmds_bray

Very similar to Jaccard results. Moderately high stress (0.15)

eDNA Ordinations Summary

The ordination that explained the most variance in the eDNA dataset was the PCoA using the Bray-Curtis dissimilarity matrix after Hellinger transformation. This is similar to the approach presented in Lacoursière‐Roussel et al. 2018. Use this representation going forward.

• Next: fit environmental vectors to this ordination to see which can be possibly explain some of the variation among samples and among species.

PCoA with Environmental Variables

Recreate, in 2D, the first two axes of the ordination (PCoA with Bray distance matrx/ Hellinger transformation) and use envfit from vegan to test and fit environmental variables.

If not making 3D plots, can do this directly in phyloseq ( example ). But phyloseq doesn’t allow for calliez correction of PCoA, so instead use example from G. Simpson to fit envfit on top of output from wcmdscale (PCoA in vegan).

Prepare the ordination variables

pcoa_bray <- wcmdscale(bray_dmat, eig = TRUE, add = "cailliez")

# trim metadata to remove samples that were removed during QC
metadata_ordinations <- metadata[metadata$SampleID %in% sample_data(ps_hellinger)$SampleID,]

# and remove repetitive metadata variables like Date/ Month/ Year/ Trawl #
metadata_ordinations <- select(metadata_ordinations, -"Year.Trawl#", -Date, -Month, -Year)

# sort metadata in same order as the distance matrix, bray_dmat
metadata_ordinations <- metadata_ordinations %>% arrange(factor(SampleID, levels = rownames(otu_table(ps_hellinger))))

# change the column name "Datecode" to "Date" (better for plotting)
colnames(metadata_ordinations)[2] <- "Date"

# fit environmental factors and save stats output
pcoa_bray_envfit <- envfit(pcoa_bray, metadata_ordinations, permutations = 1000)
capture.output(pcoa_bray_envfit, file = "stats_results/pcoa_bray_envfit.txt")

# Signficant variables include Datecode (p = 0.023976), DO (p = 0.000999), Bayside (p = 0.004995), and Station (p = 0.041958)

# Make each of the interesting variables their own ordination variables for plotting (exclude Station. This will be a color variable anyway and it's not interesting)
pcoa_bray_envfit_date <- envfit(pcoa_bray~Date, metadata_ordinations, permutations = 1000)
pcoa_bray_envfit_DO <- envfit(pcoa_bray~DO, metadata_ordinations, permutations = 1000)
pcoa_bray_envfit_Bayside <- envfit(pcoa_bray~Bayside, metadata_ordinations, permutations = 1000)

Plot in 2D

# Convert characters in metadata to factors
metadata_ordinations <- metadata_ordinations %>% mutate_if(sapply(metadata_ordinations, is.character), as.factor)
with(as.data.frame(metadata_ordinations), levels(Station))

 [1] "CORMORANT POINT"  "DUNE ROAD"        "EAST MID BAY"     "INLET"            "LITTLE POND"     
 [6] "PINE NECK"        "PONQUOGUE BRIDGE" "SHINNECOCK HILLS" "SOUTH GRASS"      "WEST MID BAY"    
[11] "WEST TIANA"      

# Define plot parameters
colvec <- c(brewer.pal(11,'Paired')) # colors of stations
shapevec <- c(19,18) # shapes indicating Bayside

# Set up basic plot
par(xpd = T, mar = par()$mar + c(0,0,0,8)) # leave space to add legend. xpd = T allows legend to be outside of the plot
# Add the site scores
with(metadata_ordinations, plot(scores(pcoa_bray, display = "sites"), col = colvec[Station], pch = shapevec[Bayside], cex = 2, xlab = "Co1 21.1%", ylab = "Co2 11.0%"))
# Add the date vector
plot(pcoa_bray_envfit_date, p.max = 0.1, lwd = 2, col = "black")

# Add the DO vector
plot(pcoa_bray_envfit_DO, p.max = 0.1, lwd = 2, col = "black")
# Add the hulls indicating Bayside
with(metadata_ordinations, ordihull(pcoa_bray, Bayside, lwd = 2, lty = c(3,5), label = FALSE))

# Add legends
with(metadata_ordinations, legend(0.77, 0.6, legend = levels(Station), col = colvec, pch = c(19,18,19,19,19,18,19,19,19,18,18), bty = "n", pt.cex = 2, cex = .8))
legend(0.77, 0.8, c("EAST", "WEST"), col = c("black"), lty = c(3,5), lwd = 2, bty = "n", cex = .8) # Legend for Bayside hull lines- did this manually

# Export using base R/ vegan helpers
setEPS()
postscript("Figures/pcoa_bray_envfit.eps", width = 7, height = 5)
par(xpd = T, mar = par()$mar + c(0,0,0,8))
with(metadata_ordinations, plot(scores(pcoa_bray, display = "sites"), col = colvec[Station], pch = shapevec[Bayside], cex = 2, xlab = "Co1 21.1%", ylab = "Co2 11.0%"))

plot(pcoa_bray_envfit_date, p.max = 0.1, lwd = 2, col = "black")
plot(pcoa_bray_envfit_DO, p.max = 0.1, lwd = 2, col = "black")
with(metadata_ordinations, ordihull(pcoa_bray, Bayside, lwd = 2, lty = c(3,5), label = FALSE))
with(metadata_ordinations, legend(0.77, 0.6, legend = levels(Station), col = colvec, pch = c(19,18,19,19,19,18,19,19,19,18,18), bty = "n", pt.cex = 2, cex = .8))
legend(0.77, 0.8, c("EAST", "WEST"), col = c("black"), lty = c(3,5), lwd = 2, bty = "n", cex = .8) 
dev.off()

quartz_off_screen 
                3 

Ordinations on CPUE

Prepare the data

Does CPUE data need to be transformed before ordinations?

• Sabel et al. 2020 do a log-transformation, then center and standardize counts before PCA
• Gothues and Able 2020 also log transform ( log(y+1) ) and center and standardize to units of standard deviation before PCA. They also excluded rare species (less than 3 occurences at all sites)
• Wegsheider et al. 2020 do no transformation, as far as I can tell, before PCoA
• Mehdi et al. 2021 use a PCoA without transformation but with a Bray-Curtis dissimilarity matrix
• Sara recommends at least a log-transformation to normalize for zeroes and high abundance/ schooling fish

Make a table of CPUE in the style of OTU tables (samples in rows/ species in columms), of the CPUE data. Trawl_counts has already been filtered so that it only includes those samples and species that are relevant to the eDNA study

CPUE_table <- trawl_counts %>%
  as_data_frame()%>%
  select(SampleID, CommonName, CPUE) %>%
  pivot_wider(names_from = CommonName, values_from = CPUE) 
CPUE_table

Transformation by log transformation (log(x+1) in order to account for zeroes)

# set NAs to zeroes
CPUE_table[is.na(CPUE_table)] <- 0

# log transform
CPUE_table_transform <- CPUE_table
CPUE_table_transform[,2:length(CPUE_table)] <- log10(CPUE_table[,2:length(CPUE_table)]+1)

CPUE_table_transform

PCA- continue here

Generate the PCA and visualize axes

head(log_transform_variances)


# Plot screeplot
ggplot(log_transform_variances, aes(x = as.numeric(PCaxis), y = PercVar)) + 
  geom_bar(stat = "identity", fill = "grey", color = "black") +
  theme_minimal() +
  theme(axis.title = element_text(color = "black", face = "bold", size = 10),
        axis.text.y = element_text(color = "black", face = "bold"),
        axis.text.x = element_blank()) +
  labs(x = "PC axis", y = "% Variance", title = "Log-Transformed PCA Screeplot, CPUE")

Total variance explained by first three axes= 28.6 + 19.5 + 13.7 = 61.8%.

##STOPPED HERE CONSIDER WHETHER TO PLOT IN 2D OR 3D

Since the second and third axes are similar, plot in 3D with 3 axes.

Visualize the PCA-

# Extract variances from the clr pca
pca_lograt_frame <- data.frame(lograt_pca$x) %>% 
  rownames_to_column(var = "SampleID")

# Merge metadata into the pcoa data table
pca_lograt_frame <- left_join(pca_lograt_frame, metadata, by = "SampleID")
head(pca_lograt_frame)

# Select eigenvalues from dataframe, round to 4 places and multiply by 100 for plotting. These will be the axes for the 3-D plot
eigenvalues<-round(lograt_variances[,2], digits = 4)*100

# Plotly - 3-D
pca_lograt <- plot_ly(pca_lograt_frame, type='scatter3d', mode='markers',
        x=~PC1,y=~PC2,z=~PC3,colors=~brewer.pal(11,'Paired'),
        color=~Station, symbols = c('circle','diamond'), symbol=~Bayside)%>%
  layout(font=list(size=12),
         title='CLR-Euclidean PCA',
         scene=list(xaxis=list(title=paste0('Co 2 ',eigenvalues[2],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    yaxis=list(title=paste0('Co 3 ',eigenvalues[3],'%'),
                               showticklabels=FALSE,zerolinecolor='black'),
                    zaxis=list(title=paste0('Co 1 ',eigenvalues[1],'%'),
                               showticklabels=FALSE,zerolinecolor='black')))
# pca_lograt

# save in "Embedded_figures" directory so that it can be hosted at Github and embedded in this notebook
withr::with_dir('Embedded_figures', htmlwidgets::saveWidget(as_widget(pca_lograt), file="pca_lograt.html", selfcontained = F))